The F-measure or F-score is one of the most commonly used " single number " measures in Information Retrieval, Natural Language Processing and Machine Learning, but it is based on a mistake, and the flawed assumptions render it unsuitable for use in most contexts! Fortunately, there are better alternatives… What the F-­‐measure is! F-measure, sometimes known as F-score or (incorrectly) the F 1 ...

This article defines the concept of wavelet-based Fisher’s information measure (wavelet FIM) and develops a closed-form expression of this measure for 1/f signals. Wavelet Fisher’s information measure characterizes the complexities associated to 1/f signals and provides a powerful tool for their analysis. Theoretical and experimental studies demonstrate that this quantity is exponentially incre...

Information retrieval studies that involve searching the Internet or marking phrases usually lack a well-defined number of negative cases. This prevents the use of traditional interrater reliability metrics like the kappa statistic to assess the quality of expert-generated gold standards. Such studies often quantify system performance as precision, recall, and F-measure, or as agreement. It can...

Computationally expensive processes, such as deductive reasoners, can suffer performance issues when they operate over large-scale data sets. The optimal procedure would allow reasoners to only operate on that information that is relevant. Procedures that approach such an ideal are necessary to accomplish the goal of commonsense reasoning, which is to endow an agent with enough background knowl...

In this work we propose a boosting-based approach to boundary detection that advances the current state-of-the-art. To achieve this we introduce the following novel ideas: (a) we use a training criterion that approximates the F-measure of the classifier, instead of the exponential loss that is commonly used in boosting. We optimize this criterion using Anyboost. (b) We deal with the ambiguous i...

We show that for an additive one-dimensional cellular automata on space of all doubly infinitive sequences with values in a finite set S = {0, 1, 2, ..., r-1}, determined by an additive automaton rule f(x ∞ f n-k, ..., xn+k) = (mod r), and a -invariant uniform Bernoulli measure μ, the measure-theoretic entropy of the additive one-dimensional cellular automata with respect to μ is equal to h ∑ −...

State of the art classification algorithms are designed to minimize the misclassification error of the system, which is a linear function of the per-class false negatives and false positives. Nonetheless non-linear performance measures are widely used for the evaluation of learning algorithms. For example, F -measure is a commonly used non-linear performance measure in classification problems. ...

F-measures are popular performance metrics, particularly for tasks with imbalanced data sets. Algorithms for learning to maximize F-measures follow two approaches: the empirical utility maximization (EUM) approach learns a classifier having optimal performance on training data, while the decision-theoretic approach learns a probabilistic model and then predicts labels with maximum expected F-me...

Receiver Operating Characteristic (ROC) analysis is a common tool for assessing the performance of various classifications. It gained much popularity in medical and other fields including biological markers and, diagnostic test. This is particularly due to the fact that in real-world problems misclassification costs are not known, and thus, ROC curve and related utility functions such as F-meas...